Prioritize Assumptions - Assumption Prioritization Skill

Skill Overview

Use the Impact × Risk matrix to systematically evaluate and prioritize product assumptions, design targeted validation experiments for each assumption, and help product teams decide what to test first and what to test later.

Applicable Scenarios

1. Product Assumption Screening

When a team is faced with a large number of assumptions to validate, this skill can quickly identify the highest-value assumptions. The Impact × Risk matrix divides assumptions into four categories, helping product managers determine which assumptions are worth investing resources to validate, which should be implemented first, and which should be abandoned outright.

2. Prioritization Decisions

When resources are limited and the team needs to determine the order in which assumptions should be tested, this skill combines the ICE and RICE frameworks to quantitatively assess the impact and risk of each assumption. It provides a data-driven basis for decision-making and avoids wasting resources due to intuition-based prioritization.

3. Assumption Validation Planning

For high-impact assumptions that require experimental validation, this skill can design minimum viable experiments, define success metrics and thresholds, and ensure that experiments effectively validate the assumptions while keeping validation costs under control.

Core Features

1. Impact × Risk Matrix Assessment

Classify assumptions based on two core dimensions:

• Impact: The value that validating the assumption can create × the number of affected customers
• Risk: (1 - Confidence) × Required effort

The assessment automatically categorizes assumptions into four quadrants: high impact/low risk (implement directly), high impact/high risk (design an experiment), low impact/low risk (defer), and low impact/high risk (reject).

2. Support for Two Frameworks

Integrates two product prioritization frameworks, ICE and RICE:

• ICE: Impact × Confidence × Ease, suitable for rapid assessment
• RICE: (Reach × Impact × Confidence) / Ease, which provides a more detailed breakdown of impact

The two frameworks can be selected flexibly and can also be used in conjunction with the prioritization-frameworks skill to obtain complete formula templates.

3. Experiment Design Recommendations

For assumptions that need to be tested, provide guidance on experiment design:

• A minimum experiment plan that maximizes learning value
• Metrics based on actual behavior rather than opinion collection
• Clear definitions of success criteria and thresholds
• Experiment results presented in the form of a prioritization matrix or table

Frequently Asked Questions

What is the difference between the Impact × Risk matrix and ICE scoring?

The Impact × Risk matrix focuses on the value and risk of validating an assumption, helping answer the question, “Is it worth testing?” ICE scoring is more commonly used to prioritize ideas that are already known to be feasible. The two can be used together: first use the matrix to screen assumptions that need testing, then use ICE to prioritize those assumptions.

When should assumption testing be postponed?

When an assumption falls into the “low impact/low risk” quadrant, testing should be postponed. The benefits of validating these assumptions are limited, and the risks are not high, so they can be considered after higher-priority assumptions have been addressed. Resources are always limited, and focusing on high-impact assumptions will yield greater returns.

How should a minimum viable experiment be designed for a high-risk assumption?

When designing a minimum experiment, focus on three points: first, use the fewest resources possible to validate the core assumption; second, measure actual user behavior rather than verbal commitments; and third, define clear success criteria in advance. For example, when validating the assumption that “users are willing to pay,” do not rely solely on surveys. Instead, set up a pre-order page and collect actual payment commitments.